Validation, Calibration and Transparency, More than Accuracy: An Appraisal of Artificial Intelligence Against...

Artificial intelligence is widely proposed against antimicrobial resistance, but how much of that literature could support clinical deployment is unclear. We searched PubMed (2016-2026) across four domains, retrieving all 2446 records. Of 725 eligible studies, 80 were appraised at full text against the artificial-intelligence appraisal extensions (PROBAST+AI, TRIPOD+AI). Selection favoured externally validated work, so the validation figures are upper bounds. Output grew from 10 studies in 2016 to 142 in 2025; within the appraised set there was no evidence that genuinely external evaluation became more likely (odds ratio 1.08/year, 95% CI 0.89-1.34). Of the 80, 45% were externally validated, 24% released a model a reader could apply, 23% assessed calibration, 16% justified a sample size, and five compared performances across patient characteristics against eight across sites; 8.8% called a random split external validation. Risk of bias was low in 16, unclear in 55 and high in nine, and none of the 18 deep-learning studies reported calibration (95% CI 0-17.6%, a loose bound on 18). We derive the design constraints these impose on a culture-independent diagnostic platform, a projection with no performance data. Validation, calibration and transparency limit this field more than accuracy does: discrimination was reported in every study appraised.